added torch.mps.empty_cache() to torch_gc()

changed a bunch of places that use torch.cuda.empty_cache() to use torch_gc() instead

extensions-builtin/LDSR/ldsr_model_arch.py

@@ -12,7 +12,7 @@ import safetensors.torch
 
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.util import instantiate_from_config, ismap
-from modules import shared, sd_hijack
+from modules import shared, sd_hijack, devices
 
 cached_ldsr_model: torch.nn.Module = None
 
@@ -112,8 +112,7 @@ class LDSR:
 
 
         gc.collect()
-        if torch.cuda.is_available:
-            torch.cuda.empty_cache()
+        devices.torch_gc()
 
         im_og = image
         width_og, height_og = im_og.size
@@ -150,8 +149,7 @@ class LDSR:
 
         del model
         gc.collect()
-        if torch.cuda.is_available:
-            torch.cuda.empty_cache()
+        devices.torch_gc()
 
         return a
 

extensions-builtin/ScuNET/scripts/scunet_model.py

@@ -85,7 +85,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
 
     def do_upscale(self, img: PIL.Image.Image, selected_file):
 
-        torch.cuda.empty_cache()
+        devices.torch_gc()
 
         try:
             model = self.load_model(selected_file)
@@ -110,7 +110,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
         np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
         del torch_img, torch_output
-        torch.cuda.empty_cache()
+        devices.torch_gc()
 
         output = np_output.transpose((1, 2, 0))  # CHW to HWC
         output = output[:, :, ::-1]  # BGR to RGB

extensions-builtin/SwinIR/scripts/swinir_model.py

@@ -42,10 +42,7 @@ class UpscalerSwinIR(Upscaler):
             return img
         model = model.to(device_swinir, dtype=devices.dtype)
         img = upscale(img, model)
-        try:
-            torch.cuda.empty_cache()
-        except Exception:
-            pass
+        devices.torch_gc()
         return img
 
     def load_model(self, path, scale=4):

modules/codeformer_model.py

@@ -99,7 +99,7 @@ def setup_model(dirname):
                             output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
                             restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
                         del output
-                        torch.cuda.empty_cache()
+                        devices.torch_gc()
                     except Exception:
                         errors.report('Failed inference for CodeFormer', exc_info=True)
                         restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))

modules/devices.py

@@ -49,10 +49,13 @@ def get_device_for(task):
 
 
 def torch_gc():
+
     if torch.cuda.is_available():
         with torch.cuda.device(get_cuda_device_string()):
             torch.cuda.empty_cache()
             torch.cuda.ipc_collect()
+    elif has_mps() and hasattr(torch.mps, 'empty_cache'):
+        torch.mps.empty_cache()
 
 
 def enable_tf32():

modules/sd_models.py

@@ -590,7 +590,6 @@ def unload_model_weights(sd_model=None, info=None):
         sd_model = None
         gc.collect()
         devices.torch_gc()
-        torch.cuda.empty_cache()
 
     print(f"Unloaded weights {timer.summary()}.")